class_docstr: Return conditioned Behavior cloning agent.
docstr: Runs a single training step.
docstr_tok: ["Runs","a","single","training","step","."]
code: def _training_step_update(self):
      """Runs a single training step."""
      self._sample_from_replay_buffer()
      states = self.preprocess_fn(self.replay_elements["state"])
  
      (self.optimizer_state, self.online_params, bc_loss) = train(
          self.network_def,
          self.online_params,
          self.optimizer,
          self.optimizer_state,
          states,
          self.replay_elements["action"],
          self.replay_elements["episode_return"],
          self._support,
      )
  
      if (
          self.training_steps > 0
          and self.training_steps % self.summary_writing_frequency == 0
      ):
          if self.summary_writer is not None:
              with self.summary_writer.as_default():
                  tf.summary.scalar(
                      "Losses/BCLoss", bc_loss, step=self.training_steps
                  )
              self.summary_writer.flush()
      if self._use_tfds:
          self.log_gradient_steps_per_epoch()
  
      self.training_steps += 1
  
imports: import functools
  from absl import logging
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.jax.agents.rainbow import rainbow_agent
  from dopamine.labs.offline_rl.jax import offline_rainbow_agent
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 2 for calling the JaxReturnConditionedBCAgent obj's underlying member method code with example usage: obj._training_step_update() without return types